Abstract: A XGBoost algorithm prediction model based on SBS （SMOTE+Bootstrap） data augmentation method was proposed to address the problem of limited data on fertilization experiments during the growth cycle of wheat （Triticum aestivum L.） and difficulty in effectively predicting fertilization using traditional prediction methods. Based on the original 135 nitrogen application data, the training set （80%） and the test set （20%） were divided. The SMOTE method was used to balance the training and test sets to obtain more feature information. Then, the Bootstrap method was used to expand the balanced data. Finally, the XGBoost prediction model was used for training and compared with other machine learning models. The results showed that using the SMOTE method to balance data significantly improved the prediction accuracy of the SBS-XGBoost model. MSE decreased from the original data of 66.802 to 13.027, MAE decreased from the original data of 6.711 to 2.393, and R² increased from the original data of 0.390 to 0.912. SBS-XGBoost not only performed well in predicting nitrogen application rates in this study, but also provided reference and guidance for scientific prediction of other small sample data.

Key words: wheat （Triticum aestivum L.）, small sample, nitrogen application rate, prediction